Abstract [en]

Several sophisticated techniques to study in vivo GI transit and regional absorption of pharmaceuticals are available and increasingly used. Examples of such methods are Magnetic Marker Monitoring (MMM) and local drug administration with remotely operated capsules. Another approach is the paracetamol and sulfapyridine double marker method which utilizes observed plasma concentrations of the two substances as markers for GI transit. Common for all of these methods is that they generate multiple types of observations e.g. tablet GI position, drug release and plasma concentrations of one or more substances. This thesis is based on the hypothesis that application of mechanistic nonlinear mixed-effect models could facilitate a better understanding of the interrelationship between such variables and result improved predictions of the processes involved in oral absorption.

Mechanistic modeling approaches have been developed for application to data from MMM studies, paracetamol and sulfapyridine double marker studies and for linking in vitro and in vivo drug release. Models for integrating information about tablet GI transit, in vivo drug release and drug plasma concentrations measured in MMM studies was outlined and utilized to describe drug release and absorption properties along the GI tract for felodipine and the investigational drug AZD0837. A mechanistic link between in vitro and in vivo drug release was established by estimation of the mechanical stress in different regions of the GI tract in a unit equivalent to rotation speed in the in vitro experimental setup. The effect of atropine and erythromycin on gastric emptying and small intestinal transit was characterized with a semi-mechanistic model applied to double marker studies in fed and fasting dogs.

The work with modeling of in vivo drug absorption has highlighted the need for, and led to, further development of mixed-effect modeling methodology with respect to model diagnostics and the handling of censored observations.

Abstract [en]

Magnetic marker monitoring (MMM) is a new technique for visualizing transit and disintegration of solid oral dosage forms through the gastrointestinal (GI) tract. The aim of this work was to develop a modeling approach for gaining information from MMM studies using data from a food interaction study with felodipine extended-release (ER) formulation. The interrelationship between tablet location in the GI tract, in vivo drug release, and felodipine disposition was modeled. A Markov model was developed to describe the tablet's movement through the GI tract. Tablet location within the GI tract significantly affected drug release and absorption through the gut wall. Food intake decreased the probability of tablet transition from the stomach, decreased the rate with which released felodipine left the stomach, and increased the fraction absorbed across the gut wall. In conclusion, the combined information of tablet location in the GI tract, in vivo drug release, and plasma concentration can be utilized in a mechanistically informative way with integrated modeling of data from MMM studies.

Abstract [en]

The purpose of this study is to investigate the impact of observations below the limit of quantification (BQL) occurring in three distinctly different ways and assess the best method for prevention of bias in parameter estimates and for illustrating model fit using visual predictive checks (VPCs). Three typical ways in which BQL can occur in a model was investigated with simulations from three different models and different levels of the limit of quantification (LOQ). Model A was used to represent a case with BQL observations in an absorption phase of a PK model whereas model B represented a case with BQL observations in the elimination phase. The third model, C, an indirect response model illustrated a case where the variable of interest in some cases decreases below the LOQ before returning towards baseline. Different approaches for handling of BQL data were compared with estimation of the full dataset for 100 simulated datasets following models A, B, and C. An improved standard for VPCs was suggested to better evaluate simulation properties both for data above and below LOQ. Omission of BQL data was associated with substantial bias in parameter estimates for all tested models even for seemingly small amounts of censored data. Best performance was seen when the likelihood of being below LOQ was incorporated into the model. In the tested examples this method generated overall unbiased parameter estimates. Results following substitution of BQL observations with LOQ/2 were in some cases shown to introduce bias and were always suboptimal to the best method. The new standard VPCs was found to identify model misfit more clearly than VPCs of data above LOQ only.

Abstract [en]

Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.

Karlsson, Mats O

Abstract [en]

In vivo prediction of drug release based on in vitro experiments is important for the development of new modified release (MR) formulations. Most efforts to improve such predictions have focused on altering the in vitro experiments to more resemble the in vivo conditions. A novel approach is evaluated in the present article where a computer model is established and used to link results from standard static in vitro experiment to in vivo predictions. A nonlinear mixed-effects model describing the in vitro drug release for 6 closely related hydrophilic matrix based MR formulations across different experimental conditions (pH, rotation speed and ionic strength) was developed. This model was applied to in vivo observations of drug release and tablet gastro intestinal (GI) position assessed with Magnetic Marker Monitoring (MMM). By combining the MMM observations with literature information on pH and ionic strength along the GI tract, the mechanical stress in different parts of the GI tract could be estimated in units equivalent to rotation speed in the in vitro set-up (USP 2 apparatus). The mechanical stress in the upper stomach was estimated to be 94 rpm and 134 rpm in the lower stomach. For the small intestine and colon the estimates of mechanical stress was 93 and 38 rpm respectively. Predictions of in vivo drug release including expected between subject/tablet variability could be made for other newly developed formulations based on the drug release model combined with a model describing tablet GI transit. This exemplifies a modeling approach that can be utilized to predict in vivo behavior from standard in vitro experiments and support formulation development and quality control of MR formulations.

Open this publication in new window or tab >>A semi-mechanistic model for characterization of regional absorption properties and prospective prediction of plasma concentrations following administration of new modified release formulations

Karlsson, Mats O

Abstract [en]

Methods to study in vivo gastro intestinal (GI) transit and/or regional absorption of pharmaceuticals are available and increasingly used in drug development. A modelling approach to utilize the information generated in such studies for prospective predictions of absorption from newly developed modified release formulations was outlined and tested for the investigational drug AZD0837. This work was a natural extension to the companion article “A semi-mechanistic model to link in vitro and in vivo drug release for modified release formulations”. The drug release model governed the amount of substance released in distinct GI regions over time. GI distribution of released drug substance, region specific rate and extent of absorption and the influence of concomitant food intake were estimated with the model. The model was informed by data from a magnetic marker monitoring study and an intubation study with local administration in colon. Disposition estimates were further supported by observations following administration of oral solution and intravenous infusion of AZD0837. Distinctly different absorption properties were characterized for different GI regions. Bioavailability over the gut-wall was estimated to be high for substance released in the stomach and absorbed in duodenum (70%) compared to substance released and absorbed in the small intestine (25%). Bioavailability was once again higher in colon (70%) but on the other hand considerably slower than in the earlier parts of the GI tract. The established model was largely successful in predicting plasma concentration following administration of three newly developed formulations for which no clinical data had been applied during model building.

Karlsson, Mats O

Abstract [en]

The paracetamol (PCM) and sulfapyridine (SP) double marker technique is based on combined gastric administration of PCM and sulfasalazine followed by plasma concentration measurements for PCM and SP. PCM is rapidly absorbed from the duodenum and can be regarded as a marker for gastric emptying (GE). Sulfasalazine is poorly absorbed from the small intestine but is extensively metabolized in the colon by bacteria into SP. As SP is only absorbed from the colon it serves as a marker for small intestinal transit time (SITT). The double marker method is used to identify and characterize effects on GE and SITT. The aim of the present investigation was to demonstrate how semi-mechanistic modeling of PCM and SP could facilitate characterization and the understanding of pharmacologically induced changes in GI transit under fed and fasting conditions. Two double marker validation studies were performed in dogs with erythromycin (1 mg/kg) and atropine (0.06 mg/kg), both of which have been described to affect GE and SITT. A semi-mechanistic nonlinear mixed-effects model was applied for simultaneous analysis of PCM and SP plasma concentrations. The model featured a compartment representing the stomach linked to a colon compartment via a series of four transit compartments representing the small intestine. Disposition of PCM and SP was described with standard 2- and 1-compartment models respectively. An essential part of the model was the inclusion of saturable first pass metabolism of PCM. This has been described before, but never taken into account when using PCM as a marker for GE. The effect of concomitant food intake on GE and SITT was found to be time dependent. Atropine and erythromycin were found to have time/concentration dependent effects on GE and SITT. As expected, erythromycin stimulated and atropine inhibited GE in the fasting state. Given the saturable first pass metabolism this resulted in almost twice as high bioavailability for PCM in erythromycin treated dogs (90%) as in atropine treated dogs (50%). Atropine treatment was primarily found to counteract the stimulatory effect of food intake on SITT, whereas erythromycin prolonged SITT under both fed and fasting conditions. Simultaneous modeling of PCM and SP was found to increase mechanistic understanding and result in plausible estimates of GE and SITT.